Europarl: A Parallel Corpus for Statistical Machine Translation

By Philipp Koehn. 2005

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Citations in the corpus

Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation (2014, neutral): “…used statistical machine translation system (SMT), the goal of the system (decoder, speciﬁcally) is to ﬁnd a translation f given a source sentence e , which maximizes where the ﬁrst term at the right hand side is called translation model and the latter language model (see, e.g., (Koehn, 2005)).”
Toolformer: Language Models Can Teach Themselves to Use Tools (2023, background): “A potential reason for GPT-J not suffering from this problem is that it was trained on more multilingual data than both OPT and GPT-3, including EuroParl (Koehn, 2005).”
Edinburgh’s Phrase-based Machine Translation Systems for WMT-14 (2014, methodology): “Languages were detected using the Compact Language Detection 23 and, except for Hindi where we lack tools, sentences were split with the Europarl sentence splitter (Koehn, 2005).”
Massively Multilingual Neural Machine Translation in the Wild: Findings and Challenges (2019, background): “( ii ) European parliamentary documents (Koehn, 2005) which include versions in 21 European languages having 1M to 2M sentence pairs.”
Learning Word Vectors for 157 Languages (2018, methodology): “For languages written using the Latin, Cyrillic, Hebrew or Greek scripts, we used the tokenizer from the Europarl preprocessing tools (Koehn, 2005).”
Findings of the 2015 Workshop on Statistical Machine Translation (2015, positive): “The alignment model was trained on the Europarl corpus (Koehn, 2005).”
XNLI: Evaluating Cross-lingual Sentence Representations (2018, background): “, 2016), for German, Greek and Bulgarian, the Europarl corpora (Koehn, 2005), for Turkish, Vietnamese and Thai, the OpenSubtitles 2018 corpus (Tiedemann, 2012), and for Hindi, the IIT Bombay corpus (Anoop et al.”
Beyond English-Centric Multilingual Machine Translation (2020, background): “Most existing bitext datasets go through English, with a few domain specific exceptions such as proceedings from international organizations (Koehn, 2005; Ziemski et al., 2016).”
Toward Gender-Inclusive Coreference Resolution (2019, background): “” Even limiting to just she and he pronouns, the bias is significant: four times as many he than she in Europarl [Koehn 2005] and the Common Crawl [Smith et al. 2013], six times as many in News Commentaries [Tiedemann 2012], and fifty times as many in Hong Kong Laws corpus.”
GPT-NeoX-20B: An Open-Source Autoregressive Language Model (2022, background): “It consists of data from 22 data sources, coarsely broken down into 5 categories: • Academic Writing: Pubmed Abstracts and PubMed Central, arXiv, FreeLaw,5 USPTO Backgrounds,6 PhilPapers,7 NIH Exporter8 • Web-scrapes and Internet Resources: CommonCrawl, OpenWebText2, StackExchange,9 Wikipedia (Engli”
Self-training Improves Pre-training for Natural Language Understanding (2020, background): “, 2019) and Europarl (Koehn, 2005) parallel data from English to French, Italian and Indonesian - language pairs that were shown to provide good paraphrastic sentence embeddings (Wieting et al.”
Input-Tuning: Adapting Unfamiliar Inputs to Frozen Pretrained Models (2022, methodology): “We use the parallel corpus from version 7 of Europarl corpus (Koehn et al., 2005) as the training data for all three translation tasks.”
MURAL: Multimodal, Multitask Retrieval Across Languages (2021, methodology): “We create an Ensemble of Open Bilingual Translation (EOBT) Pairs dataset by combining publicly available datasets, including Europarl (Koehn, 2005), Paracrawl (Esplà et al.”
What you can cram into a single $&!#* vector: Probing sentence embeddings for linguistic properties (2018, methodology): “We train Neural Machine Translation systems on three language pairs using about 2M sentences from the Europarl corpora (Koehn, 2005).”
Efficient softmax approximation for GPUs (2016, background): “• Europarl2 is a machine translation corpus, containing 20 languages [25].”
Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings (2018, methodology): “Unless otherwise indicated, all experiments use an English/French/Spanish/German multilingual encoder trained on Europarl v7 (Koehn, 2005) for 10 epochs as described in Section 3.”
Lessons from archives: strategies for collecting sociocultural data in machine learning (2019, background): “ears as the standard for Part-of-Speech tags [45]. Others include OntoNotes for coreference resolution and named entity recognition (NER) and the EuroParl parallel corpus for machine translation (MT) [30, 40]. Sometimes, researchers and practitioners build on top of existing datasets motivated by av”
A Survey on Bias and Fairness in Machine Learning (2019, background): “translating them into English, and showing that Google Translate shows favoritism toward males for stereotypical fields such as STEM jobs. In [122] authors annotated and analyzed the Europarl dataset [71], a large political, multilingual dataset used in machine translation, and discovered that with ”